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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""AffineLinearOperator Tests."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.contrib import linalg
from tensorflow.contrib.distributions.python.ops.bijectors import affine_linear_operator as affine_linear_operator_lib
from tensorflow.python.platform import test
class AffineLinearOperatorTest(test.TestCase):
def testIdentity(self):
with self.test_session():
affine = affine_linear_operator_lib.AffineLinearOperator(
validate_args=True)
x = np.array([[1, 0, -1], [2, 3, 4]], dtype=np.float32)
y = x
ildj = 0.
self.assertEqual(affine.name, "affine_linear_operator")
self.assertAllClose(y, affine.forward(x).eval())
self.assertAllClose(x, affine.inverse(y).eval())
self.assertAllClose(ildj, affine.inverse_log_det_jacobian(y).eval())
self.assertAllClose(-affine.inverse_log_det_jacobian(y).eval(),
affine.forward_log_det_jacobian(x).eval())
def testDiag(self):
with self.test_session():
shift = np.array([-1, 0, 1], dtype=np.float32)
diag = np.array([[1, 2, 3],
[2, 5, 6]], dtype=np.float32)
scale = linalg.LinearOperatorDiag(diag, is_non_singular=True)
affine = affine_linear_operator_lib.AffineLinearOperator(
shift=shift, scale=scale, validate_args=True)
x = np.array([[1, 0, -1], [2, 3, 4]], dtype=np.float32)
y = diag * x + shift
ildj = -np.sum(np.log(np.abs(diag)), axis=-1)
self.assertEqual(affine.name, "affine_linear_operator")
self.assertAllClose(y, affine.forward(x).eval())
self.assertAllClose(x, affine.inverse(y).eval())
self.assertAllClose(ildj, affine.inverse_log_det_jacobian(y).eval())
self.assertAllClose(-affine.inverse_log_det_jacobian(y).eval(),
affine.forward_log_det_jacobian(x).eval())
def testTriL(self):
with self.test_session():
shift = np.array([-1, 0, 1], dtype=np.float32)
tril = np.array([[[1, 0, 0],
[2, -1, 0],
[3, 2, 1]],
[[2, 0, 0],
[3, -2, 0],
[4, 3, 2]]],
dtype=np.float32)
scale = linalg.LinearOperatorTriL(tril, is_non_singular=True)
affine = affine_linear_operator_lib.AffineLinearOperator(
shift=shift, scale=scale, validate_args=True)
x = np.array([[[1, 0, -1],
[2, 3, 4]],
[[4, 1, -7],
[6, 9, 8]]],
dtype=np.float32)
# If we made the bijector do x*A+b then this would be simplified to:
# y = np.matmul(x, tril) + shift.
y = np.squeeze(np.matmul(tril, np.expand_dims(x, -1)), -1) + shift
ildj = -np.sum(np.log(np.abs(np.diagonal(
tril, axis1=-2, axis2=-1))),
axis=-1)
self.assertEqual(affine.name, "affine_linear_operator")
self.assertAllClose(y, affine.forward(x).eval())
self.assertAllClose(x, affine.inverse(y).eval())
self.assertAllClose(ildj, affine.inverse_log_det_jacobian(y).eval())
self.assertAllClose(-affine.inverse_log_det_jacobian(y).eval(),
affine.forward_log_det_jacobian(x).eval())
if __name__ == "__main__":
test.main()
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